
Donut [1]}. The model proposed very recently is based on VAE, which takes a fixed length sliding windows of an uni-variate time series as input. To capture `normal patterns' of data, the modified ELBO technique is devised to leverage generative model \(p_{\theta _{}}(\mathbf {x}|\mathbf {z})\)  and latent regularization \(p_{\theta _{}}(\mathbf {z})\)  of VAE, such that the model is encouraged to learn only normal patterns.

STORN [2]}. As a sequential version of VAE, the model is based on stochastic recurrent neural network (STORN). Different from SISVAE, the sequences of latent variables \(\mathbf {z}_t\)  are generated independently. The model uses lower bound output as anomaly score.

